Machine learning-based systems and methods for predicting a digital activity and automatically executing digital activity-accelerating actions

ABSTRACT

A method for machine learning-informed surfacing and automated execution of digital activity-accelerating actions includes identifying a target digital artifact; and based on identifying the target digital artifact: searching a digital activity-accelerator registry based on the target digital artifact; and in accordance with a determination that the digital activity-accelerator registry includes a composite activity sequence corresponding to the target digital artifact, displaying, via a graphical user interface, one or more selectable representations of one or more tasks included in the composite activity sequence.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/325,602, filed on 30 Mar. 2022, which is incorporated in its entiretyby this reference.

TECHNICAL FIELD

This invention relates generally to the computer-aided task automationfield, and more specifically, to new and useful systems and methods forintelligently identifying and automatically executingactivity-accelerating actions that may be associated with a digitalartifact.

BACKGROUND

More and more businesses are providing their workforce with access toelectronic messaging applications. Some of the digital messages includedin these electronic messaging applications may relate toactivities/tasks that need to be performed in various othercomputed-based applications. Since these electronic messagingapplications and the various other computed-based applications oftenoperate in a disjoined manner, it is left to the user to determine whichactivities/tasks are related to a target electronic message and, inturn, manually perform such activities/tasks at the various othercomputed-based applications.

This analysis may be burdensome to the user and prone to human error,especially when there are numerous tasks and/or complex tasks associatedwith an electronic message. Thus, there is a need for new and usefulsystems and methods for automatically identifying activities/tasks thatmay be associated with an electronic message and enabling automaticexecution of such activities/tasks.

The embodiments of the present application described herein providetechnical solutions that address, at least, the needs described above.

BRIEF SUMMARY OF THE INVENTION(S)

In some embodiments, a method for machine learning-informed surfacingand automated execution of digital activity-accelerating actionsincludes: constructing a digital activity-accelerator registry thatincludes a plurality of composite activity sequences, wherein each ofthe plurality of composite activity sequences relates to a distinctcollection of interdependent computer-executable tasks; identifying atarget digital artifact; and based on identifying the target digitalartifact: computing, via an ensemble of machine learning models, aplurality of activity-accelerating inferences for the target digitalartifact, including: (1) a first activity-accelerating inference thatrelates to an estimated intent of the target digital artifact, (2) asecond activity-accelerating inference that relates to an estimateddomain of an activity associated with the target digital artifact, and(3) a third activity-accelerating inference that relates to an estimatedsub-domain of the activity associated with the target digital artifact;searching the digital activity-accelerator registry based on theplurality of activity-accelerating inferences, wherein searching thedigital activity-accelerator registry includes: constructing a compositeactivity sequence search query that comprises a plurality of searchparameters, wherein the plurality of search parameters includes: (A) afirst search parameter that causes the search query, when executed, tosearch for composite activity sequences in the digitalactivity-accelerator registry that include a computer-executable taskrelating to the estimated intent of the target digital artifact, (B) asecond search parameter that causes the search query, when executed, tosearch for composite activity sequences in the digitalactivity-accelerator registry that relate to the estimated domain of theactivity associated with the target digital artifact, and (C) a thirdsearch parameter that causes the search query, when executed, to searchfor composite activity sequences in the digital activity-acceleratorregistry that relate to the estimated sub-domain of the activityassociated with the target user artifact; and executing the compositeactivity sequence search query, wherein executing the composite activitysequence causes one or more target composite activity sequences to bereturned from the digital activity-accelerator registry if the one ormore target composite activity sequences satisfy the plurality of searchparameters; extracting at least a subset of the computer-executabletasks included in the one or more target composite activity sequencesbased on one or more pre-defined action-surfacing criteria; anddisplaying, via a graphical user interface, a digital representationcorresponding to each computer-executable task included in the subset,wherein each digital representation displayed in the graphical userinterface is selectable and, when selected, causes a correspondingactivity-accelerating task to be automatically executed by one or morecomputers.

In some embodiments, constructing the digital activity-acceleratorregistry includes automatically creating, by the one or more computers,the plurality of composite activity sequences.

In some embodiments, automatically creating the plurality of compositeactivity sequences includes: collecting digital activity data relatingto one or more users over a respective period of time, grouping thedigital activity data into one or more sets of interdependentcomputer-executable tasks, instantiating a composite activity sequencecorresponding to each of the one or more sets of interdependentcomputer-executable tasks, and embedding each instantiated compositeactivity sequence in the digital activity-accelerator registry.

In some embodiments, the digital activity data includes a firstplurality of computer-executable tasks, and grouping the digitalactivity data into the one or more sets of interdependentcomputer-executable tasks includes: in accordance with a determinationthat the one or more users performed the first plurality ofcomputer-executable tasks in a same order for more than a thresholdnumber of times, grouping the first plurality of computer-executabletasks as a first set of interdependent computer-executable tasks; and inaccordance with a determination that the one or more users did notperform the first plurality of computer-executable tasks in a same orderfor more than the threshold number of times, forgoing grouping the firstplurality of computer-executable tasks.

In some embodiments, the digital activity-accelerator registry includes(1) a plurality of composite activity sequences automatically derived bythe one or more computers and (2) a plurality of user-created compositeactivity sequences.

In some embodiments, the one or more target composite activity sequencesreturned from executing the composite activity sequence search queryincludes a first target composite activity sequence, and extracting thesubset of computer-executable tasks from the first target compositeactivity sequence includes: (1) extracting, from the first targetcomposite activity sequence, a first computer-executable taskcorresponding to the estimated intent of the target digital artifact,and (2) extracting, from the first target composite activity sequence,one or more second computer-executable tasks ordered after thecomputer-executable task corresponding to the estimated intent of thetarget digital artifact.

In some embodiments, computer-executable tasks ordered before the firstcomputer-executable task are not extracted from the first targetcomposite activity sequence.

In some embodiments, the one or more target composite activity sequencessatisfy the plurality of search parameters if: (1) the one or moretarget composite activity sequences include a computer-executable taskrelating to the estimated intent of the target digital artifact, (2) theone or more target composite activity sequences relate to the estimateddomain of the activity associated with the target digital artifact, and(3) the one or more target composite activity sequences relate to theestimated sub-domain of the activity associated with the target digitalartifact.

In some embodiments, the method is performed at a computer-aided taskautomation service, and the target digital artifact comprises a digitalemail message.

In some embodiments, the target digital artifact is identified within athreshold amount of time of a digital messaging application sending thedigital email message.

In some embodiments, the target digital artifact is identified within athreshold amount of time of a digital messaging application receivingthe digital email message.

In some embodiments, displaying the graphical user interface includesdisplaying the digital representation corresponding to eachcomputer-executable task included in the subset concurrently with adigital representation of the target digital artifact.

In some embodiments, the graphical user interface includes a firstdigital representation corresponding to a first computer-executabletask. In some embodiments, the method includes receiving an inputselecting the first digital representation; and based on receiving theinput: searching a lookup table that correlates computer-executabletasks to document templates; and in accordance with a determination thatthe lookup table includes an entry corresponding to the firstcomputer-executable task, automatically generating one or moreuser-specific documents.

In some embodiments, the method includes in accordance with adetermination that the lookup table does not include the entrycorresponding to the first computer-executable task, forgoingautomatically generating one or more user-specific documents.

In some embodiments, automatically generating one or more user-specificdocuments includes: identifying, based on the entry corresponding to thefirst computer-executable task, a plurality of document templatescorresponding to the first computer-executable task; instantiating,based on the plurality of document templates, a plurality ofuser-specific documents; extracting, via an entity extraction machinelearning model, a plurality of named entities from the target digitalartifact; and installing, at corresponding portions in the plurality ofuser-specific documents, one or more of the plurality of named entities.

In some embodiments, a method for machine learning-informed surfacingand automated execution of digital activity-accelerating actionsincludes: identifying a target digital artifact; and based onidentifying the target digital artifact: searching a digitalactivity-accelerator registry based on the target digital artifact; andin accordance with a determination that the digital activity-acceleratorregistry includes a composite activity sequence corresponding to thetarget digital artifact, displaying, via a graphical user interface, oneor more selectable representations of one or more tasks included in thecomposite activity sequence.

In some embodiments, the method includes in accordance with adetermination that the digital activity-accelerator registry does notinclude a composite activity sequence corresponding to the targetdigital artifact, forgoing displaying the one or more selectablerepresentations.

In some embodiments, displaying the graphical user interface includesdisplaying a first representation corresponding to the target digitalartifact, and the target digital artifact is identified when an inputselecting the first representation is received.

In some embodiments, the method includes receiving a second inputselecting a second representation corresponding to a second digitalartifact; and based on receiving the second input: forgoing displayingthe one or more selectable representations of the one or more tasksincluded in the composite activity sequence; searching the digitalactivity-accelerator registry based on the second digital artifact; andin accordance with a determination that the digital activity-acceleratorregistry includes a second composite activity sequence corresponding tothe second digital artifact, displaying, via a graphical user interface,one or more selectable representations of one or more tasks included inthe second composite activity sequence.

In some embodiments, the target digital artifact comprises a digitalemail message.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a schematic representation of a system 100 inaccordance with one or more embodiments of the present application;

FIG. 2 illustrates an example method 200 in accordance with one or moreembodiments of the present application; and

FIG. 3 illustrates an example schematic for searching a digitalactivity-accelerator registry in accordance with one or more embodimentsof the present application.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionsare not intended to limit the inventions to these preferred embodiments,but rather to enable any person skilled in the art to make and use theseinventions.

1. System for Machine Learning-Based Pairing of UserStimuli-to-Activity-Accelerating Actions

As shown in FIG. 1 , a system 100 for machine learning-based pairing ofuser stimuli-to-digital profiles and/or pairing of userstimuli-to-activity-accelerating actions may include a user interface110, a machine learning-based classification and inference system 120, asearch query construction module 130, a subdomain-informed query engine140, and a digital profile pairing engine 150. The system 100 mayoptionally include an automatic speech recognition module 115. Thesystem 100 may sometimes be referred to herein as a service providerdiscovery service 100, a user content-to-digital account pairing service100, or an online digital profile discovery service 100. As described inmore detail in U.S. patent Ser. No. 17/687,229, which is incorporated inits entirety by this reference, the service provider discovery service100 may enable a discovery of any suitable online digital profile dataand/or related online digital profile content for a plurality ofdistinct service provider digital profiles including, but not limitedto, lawyer-service provider profiles, health care-service providerprofiles, financial services-service provider profiles, insuranceservices-service provider profile, and/or the like.

In one or more embodiments, each module or engine of the system 100 maybe implemented by one or more computing servers, one or more computingprocessors, or computing servers of a distributed computing system.

1.1 User Interface

In one or more embodiments, the system or service 100 may function toimplement a user interface 110 that may preferably function to identify,collect, or ingest user input in any form. The user interface 110 maycomprise a search interface that may be digitally accessible to onlineusers over a computing medium, such as the world wide web or theinternet. Additionally, or alternatively, in some embodiments, the userinterface 110 may comprise one or more selectable user interfaceelements that, when selected, cause the system 100 to automaticallyperform one or more activity-accelerating actions (as described in moredetail in method 200).

In one or more embodiments, the online users that may be interactingwith the user interface 110 may input a user query in the form of textinput, utterance input, and/or image input, and the user interface 110may function to identify, collect, or ingest the user query.

In one or more embodiments, the user interface 110 may be implementedvia any suitable computing device and/or from including, but not limitedto, a mobile computing device, a personal computing device, aweb-browser (having a website displayed therein), or the like. In someembodiments, the user interface 110 may function to implement one ormore graphical user interface objects that may enable online users tocontinuously or periodically interact with the system 100 via the userinterface 110. For instance, the user interface 110 may function toimplement one or more text input fields into which online users mayfreely (e.g., manually) enter a user query (e.g., a user stimulus, auser input, or the like). In one or more embodiments, the user interface100 may be enabled by a client application operating on a mobilecomputing device or the like. In such embodiments, the clientapplication may be in operable communication with a client server of thesystem 100.

In one or more embodiments, based on identifying input of the user queryat the user interface 110 (e.g., an Internet-accessible user interface),the user query data associated with the user query may be routed to amachine learning-based classification system and, in some embodiments,the user query data may be optionally routed to an automatic speechrecognition module 115 that may convert the user query to text beforerouting the user query data to the machine learning-based classificationsystem, if needed.

1.2 Machine Learning-Based Inference System|Machine Learning-BasedDigital Profile Subdomain Inference System

In one or more embodiments, the system 100 may function to implement amachine learning-based digital profile inference system 120 that maypreferably function to generate inferences (e.g., classificationinferences, including classification labels, entity or slot extractioninferences, and/or the like) (or classify) a target piece of userstimulus data (e.g., pre-processed user stimulus data) into one or moredigital profile subdomain categories. Additionally, or alternatively,the inference system 120 may preferably function to generateactivity-accelerating inferences for a target digital artifact (e.g., adigital message) (as described in more detail in method 200).

The machine learning-based digital profile inference system 120, whichmay be sometimes referred to herein as a machine learning-based digitalprofile classification system 120 may be trained for interpreting theuser query (e.g., human text), extracting features from the user query,and/or computing digital profile subdomain classification predictionsbased on the extracted features.

In one or more embodiments, an algorithmic structure underlying themachine learning-based digital profile classification system 120 may bea multi-class digital profile subdomain classification model or anensemble of digital profile classification models. In one or moreembodiments, the multi-class digital profile subdomain classificationmodel may be algorithmically configured and/or specifically trained togenerate predictions and/or inferences across a plurality of distinctcategories or classes of distinct digital profiles. Accordingly, in suchembodiments, the multi-class classification model may function to searchunique combination of distinct classes of profiles based on search queryinput data. The multi-class digital profile subdomain classificationmodel or the ensemble of digital profile classification models mayemploy any suitable machine learning including one or more of:supervised learning (e.g., using logistic regression, using backpropagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), adversarial learning, and any other suitable learning style.Each module of the plurality can implement any one or more of: aregression algorithm (e.g., ordinary least squares, logistic regression,stepwise regression, multivariate adaptive regression splines, locallyestimated scatterplot smoothing, etc.), an instance-based method (e.g.,k-nearest neighbor, learning vector quantization, self-organizing map,etc.), a regularization method (e.g., ridge regression, least absoluteshrinkage and selection operator, elastic net, etc.), a decision treelearning method (e.g., classification and regression tree, iterativedichotomiser 3, C4.5, chi-squared automatic interaction detection,decision stump, random forest, multivariate adaptive regression splines,gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes,averaged one-dependence estimators, Bayesian belief network, etc.), akernel method (e.g., a support vector machine, a radial basis function,a linear discriminate analysis, etc.), a clustering method (e.g.,k-means clustering, density-based spatial clustering of applicationswith noise (DBSCAN), expectation maximization, etc.), a bidirectionalencoder representation form transformers (BERT) for masked languagemodel tasks and next sentence prediction tasks and the like, variationsof BERT (i.e., ULMFiT, XLM UDify, MT-DNN, SpanBERT, RoBERTa, XLNet,ERNIE, KnowBERT, VideoBERT, ERNIE BERT-wwm, MobileBERT, TinyBERT, GPT,GPT-2, GPT-3, GPT-4 (and all subsequent iterations), ELMo, content2Vec,and the like), an associated rule learning algorithm (e.g., an Apriorialgorithm, an Eclat algorithm, etc.), an artificial neural network model(e.g., a Perceptron method, a back-propagation method, a Hopfieldnetwork method, a self-organizing map method, a learning vectorquantization method, etc.), a deep learning algorithm (e.g., arestricted Boltzmann machine, a deep belief network method, aconvolution network method, a stacked auto-encoder method, etc.), adimensionality reduction method (e.g., principal component analysis,partial least squares regression, Sammon mapping, multidimensionalscaling, projection pursuit, etc.), an ensemble method (e.g., boosting,bootstrapped aggregation, AdaBoost, stacked generalization, gradientboosting machine method, random forest method, etc.), and any suitableform of machine learning algorithm. Each processing portion of thesystem 100 can additionally or alternatively leverage: a probabilisticmodule, heuristic module, deterministic module, or any other suitablemodule leveraging any other suitable computation method, machinelearning method or combination thereof. However, any suitable machinelearning approach can otherwise be incorporated in the system 100.Further, any suitable model (e.g., machine learning, non-machinelearning, etc.) may be implemented in the various systems and/or methodsdescribed herein.

In one or more embodiments, the classification inference(s) of themachine learning-based digital profile classification system 120 mayfunction to label a target piece of user stimulus data into one or moredigital profile subdomain categories that may be used, as input, to adownstream module (e.g., the search query construction module) or engine(e.g., the digital profile pairing engine, the subdomain-informed queryengine).

1.3 Search Query Construction Module

In one or more embodiments, the system 100 may function to implement asearch query construction module 130 that may preferably function toconstruct one or more search queries for a target piece of user stimulusdata and/or function to construct one or more search queries foridentifying activity-accelerating actions germane to a target digitalartifact (as described in more detail in method 200). In one or moreembodiments, the search query construction module may function to derivesearch parameters (for a target piece of user stimulus data) based onthe machine learning-based classification label(s) or machinelearning-based classification inference(s) predicted by the machinelearning-based digital profile classification system 120. In one or moreembodiments, the one or more search queries constructed by the searchquery construction module 130 may be in a machine-understandable formator syntax according to a prescribed search format or search syntaxrequired by at least one of the digital profile pairing engine 150 orthe subdomain-informed query engine 140.

In one or more embodiment, the search query construction module mayfunction to construct a subdomain-informed search query that, whenexecuted, may function to search a database comprising a corpus ofsubdomain-informed query data and match (or pair) the subdomain-informedsearch query to one or more subdomain-informed queries based on thesearch parameters of the subdomain-informed query. Additionally, oralternatively, in one or more embodiments, the search query constructionmodule 130 may function to construct a digital profile search query (ordigital account search query) that, when executed, may function toautomatically search a database comprising a corpus of digital profiledata and pair (or match) the digital profile search query to one or moredigital profiles data sets (e.g., a plurality of service providerdigital profiles, a plurality of digital accounts, or the like).

1.4 Subdomain-Informed Query Engine

In one or more embodiments, the system 100 may function to implement asubdomain-informed query engine 140 that may preferably function toselectively identify and pose one or more subdomain-informed queries toa target online user of the system or service 100. In one or moreembodiments, the subdomain-informed query engine 140 may comprise adatabase comprising a corpus of subdomain-informed query data. In one ormore embodiments, the corpus of subdomain-informed query data may besearched using the subdomain-informed search query (e.g., the searchparameters of the subdomain-informed search query) constructed by thesearch query construction module 130. The corpus of subdomain-informedquery data may include subdomain-informed queries indexed according to acorresponding digital profile subdomain classification label (e.g., thecorpus of subdomain query data may include a plurality of distinctdigital profile subdomain classification labels and a distinct set ofsubdomain-informed queries digitally mapped (or electronically linked)to each of the plurality of distinct digital profile subdomainclassification labels).

In one or more embodiments, the subdomain-informed query engine 140 mayfunction to selectively match or selectively pair a subset ofsubdomain-informed queries of the plurality of subdomain-informedqueries of the corpus of subdomain-informed query data to a targetsubdomain-informed search query. Accordingly, in one or moreembodiments, the subset of subdomain-informed query data may be posed tothe target user via the user interface 110.

1.5 Digital Profile Pairing Engine

In one or more embodiments, the system 100 may function to implement adigital profile pairing engine 150 that may preferably function toselectively identify and display, via an Internet-accessible userinterface 110, one or more digital profiles to a target online user ofthe system or service 100. In one or more embodiments, the digitalprofile pairing engine 150 may comprise a database comprising a corpusof digital profile data. In one or more embodiments, the corpus ofdigital profile data may be searched using the digital profile searchquery (e.g., the search parameters of the digital profile search query)constructed by the search query construction module 130. The corpus ofdigital profile data may include digital profiles or digital accountsindexed according to the one or more distinct digital profile subdomainclassification labels (as described in more detail in U.S. patent Ser.No. 17/687,229).

In one or more embodiments, the digital profile pairing engine 150 mayfunction to selectively match or selectively pair a subset of digitalprofiles (e.g., digital accounts) of the plurality of digital profiles(e.g., digital accounts) of the corpus of digital profile data to atarget digital profile search query that may be displayed to a targetuser via the user interface 110.

It shall be noted that, in some examples, the system 100 may perform oneor more of the processes described in method 200 with additional, fewer,or different components than the ones described above.

2. Machine Learning-Based Method for Intelligently Surfacing andExecuting Digital Activity-Accelerating Actions

As shown in FIG. 2 , a machine learning-based method 200 forintelligently surfacing and executing digital activity-acceleratingactions preferably includes constructing a digital activity-acceleratorregistry (S210), computing one or more activity-accelerating inferencesfor a target user artifact (S220), identifying activity-acceleratingactions germane to the target user artifact (S230), and executing one ormore of the identified activity-accelerating actions (S240).

2.10 Constructing a Digital Activity-Accelerator Registry

S210, which includes constructing a digital activity-acceleratorregistry, may function to instantiate a digital activity-acceleratorregistry that may be configured to store data relating to one or morecomposite activity sequences and/or may function to embed one or moreadditional composite activity sequences in the digitalactivity-accelerator registry preferably in association with one or moreactivity or domain classification labels. In some embodiments, eachcomposite activity sequence embedded in the digital activity-acceleratorregistry may relate to a distinct objective or goal of a user and/or maycomprise a collection of interdependent or cascading tasks/actionsrequired for completing such objective or goal (e.g.,computer-executable tasks/actions, user-performed tasks/actions, and/orthe like). The digital activity-accelerator registry, in variousembodiments, may include any suitable data structure including, but notlimited to, a lookup table, a lookup matrix, and/or the like.

In some embodiments, S210 may function to construct the digitalactivity-accelerator registry for a target subscriber (e.g., asubscriber of system 100 or service implementing the method 200).Accordingly, in such embodiments and as will be described in more detailherein, when constructing the digital activity-accelerator registry fora target subscriber, S210 may function to embed, in the digitalactivity-accelerator registry, composite activity sequences that may bebased on one or more users associated with the target subscriber (and,optionally, not based on users associated with other subscribers of thesystem 100).

System-Derived Composite Activity Sequences

In a first implementation, embedding the one or more composite activitysequences in the digital activity-accelerator registry may includeembedding one or more system-derived composite activity sequences in thedigital activity-accelerator registry (e.g., composite activitysequences derived without user input).

In some embodiments, automatically deriving one or more system-derivedcomposite activity sequences may include collecting or obtaining digitalactivity data relating to digital activities of one or more usersassociated with a target subscriber. In one example of such embodiments,the digital activity data may be collected/sourced via an on-devicesoftware agent installed at one or more electronic devices of the one ormore users and/or may be provided (e.g., uploaded) to the system 100 bya target subscriber. Furthermore, in some examples of such embodiments,the digital activity data sourced/collected by S210 may include datarelating to applications used by the one or more users over one or moreperiods, data relating to user interfaces (e.g., application windows)accessed by the one or more users over the respective time period(s),data relating to operating system (OS) commands executed by the one ormore users over the respective time period, data relating to userinterface elements selected by the one or more users over the respectivetime period, data relating to text entered/edited at the one or moreuser interfaces over the respective time period, data relating to theURLs visited by the one or more users over the respective time period,data relating to an amount of time spent at a target user interface overthe respective time period, data relating to computer activitiesperformed contemporaneously with, before, and/or after executing arespective computer command, and/or the like.

Additionally, or alternatively, in some embodiments, automaticallyderiving one or more system-derived composite activity sequences mayinclude identifying/recognizing one or more collections (e.g., sets) ofinterdependent activities or tasks from the sourced digital activitydata. In a first implementation of such embodiments, S210 may functionto recognize/detect one or more sets of interdependent activities ortasks based on pre-defined rules or heuristics. For instance, in anon-limiting example, S210 may function to automatically determine thata first set of activities/tasks included in the sourced digital activitydata are interdependent based on S210 detecting that more than athreshold number of users executed the first set of activities/tasks ina same (or similar) sequence/order. Similarly, in another non-limitingexample, S210 may function to automatically determine that a second setof activities/tasks included in the sourced digital activity data areinterdependent based on S210 detecting that a user performed the secondset of activities/tasks in a same (or similar) sequence for more than athreshold number of times.

Accordingly, in or more embodiments, S210 may function to perform apairwise analysis of a first corpus of digital activity data of a firstuser and a second corpus of digital activity data of a second user toidentify common sets of tasks or activities being executed or performedin a similar sequence. In such embodiments, S210 may function to extractfeatures of the common sets of tasks from the corpora of digitalactivity data to derive one or more composite activity sequences.

It shall be recognized that, in some embodiments, S210 may functiondetect interdependent activities/tasks based on a combination ofheuristics and/or based on other heuristics without departing from theinventions contemplated herein.

Additionally, or alternatively, in some embodiments, automaticallyderiving one or more system-derived composite activity sequences mayinclude constructing a composite user activity sequence for eachcollection or set of interdependent activities/tasks identified by S210.In one example of such embodiments, to construct a composite activitysequence for an identified collection or set of interdependentactivities/tasks, S210 may function to instantiate a new compositeactivity sequence data structure and add, to the instantiated datastructure, each activity/task included in the identified set ofinterdependent activities/tasks.

Furthermore, in some examples, constructing a system-derived compositeactivity sequence for a target set of interdependent computer-executableactivities/tasks may include ordering each activity/task added to theinstantiated data structure. It shall be noted that, in someembodiments, the order specified/indicated in the composite activitysequence data structure may be equivalent to (or different from) theorder/sequence in which the activities/tasks were originally executed bythe one or more users (as mentioned above).

Surfacing the System-Derived Composite Activity Sequences

In some embodiments, S210 may function to surface one or more of thesystem-derived composite activity sequences to a user for review, input,and/or validation. In one example of such embodiments, surfacing one ormore of the system-derived composite activity sequences may includedisplaying graphical representations of the system-derived compositeactivity sequences at user interfaces provided by the system 100. Itshall be noted that, in some examples, graphically displaying asystem-derived composite activity sequence to a user may enable the userto easily review the system-derived composite activity sequence and, inturn, modify and/or add the system-derived composite activity sequenceto the digital activity-accelerator registry.

In some embodiments, while S210 is surfacing one or more of thesystem-derived composite activity sequences to the user, S210 mayadditionally function to receive user input for assigning a label/nameto each (or at least one) of the system-derived composite activitysequences, user input for assigning a practice area to each of (or atleast one) of the system-derived composite activity sequences, and/oruser input for assigning a sub-practice area to each of (or at leastone) of the system-derived composite activity sequences.

For instance, in a non-limiting example, based on a user review of asystem-derived composite activity sequence, S210 may function to receiveuser input for indicating that the system-derived composite activitysequence includes activities/tasks related to intaking a new client in acriminal practice area (or other practice area), related to requestingmedical records in a personal injury practice area, related torequesting financial records in a corporate practice area, and/or thelike.

Furthermore, in some embodiments, while S210 is surfacing asystem-derived composite activity sequence to the user, S210 mayfunction to receive one or more user inputs for assigning a label/nameto each (or at least one) of the activities/tasks underpinning thesystem-derived composite activity sequence and/or receive user inputsfor modifying an order of the activities/tasks underpinning thesystem-derived composite activity. Additionally, or alternatively, insome embodiments, the received user inputs may relate to inputs foradding additional activities/tasks to the system-derived compositeactivity sequence, removing activities/tasks to the system-derivedcomposite activity sequence, and/or the like.

For instance, in a non-limiting example, based on a user review of asystem-derived composite activity sequence, S210 may function to receivean input indicating that that a first activity/task of thesystem-derived composite activity sequence relates to sending anengagement letter to a client via email, that a second activity/task ofthe system-derived composite activity sequence relates to following-upwith the client via email, that a third activity/task of thesystem-derived composite activity sequence relates to obtaining a signedengagement letter from the client via email, that a fourth activity/taskof the system-derived composite activity sequence relates to scanningthe signed engagement letter into a file/directory/storage locationassociated with the client, that a fifth activity/task of thesystem-derived composite activity sequence relates to reviewing theclient's case file, and/or the like.

User-Derived Composite Activity Sequences

In a second implementation, embedding one or more composite activitysequences in the digital activity-accelerator registry may includeembedding one or more user-derived (or user-created) composite activitysequences in the digital activity-accelerator registry. In one exampleof such implementations, S210 may function to construct the one or moreuser-derived composite activity sequences based on one or more inputsreceived by a user.

In some embodiments of such an example, S210 may function to collect theone or more user inputs by generating and displaying one or more one ormore task-related prompts and/or task-related input objects viagraphical user interfaces. The information that may be collected, viathe one or more graphical user interfaces, may include, but may not belimited to, a user-provided name/label for the composite activitysequence being created (e.g., “New Client Intake”), a practice areaassociated with the composite activity sequence being created (e.g.,“Intellectual Property”), a sub-practice area associated with thecomposite activity sequence being created (e.g., “Patents”), a list ofuser-performed or computer-executable task/actions that correspond tothe composite activity sequence being created (e.g., sending anengagement letter, creating a new client matter in the subscriber'sdocument management system, etc.), the order in which the list oftasks/actions are to be performed, and/or the like.

Furthermore, in some embodiments, the above-described graphical userinterface(s) may additionally, or alternatively, include a userinterface element that, when selected, causes S210 to create a compositeactivity sequence according to the provided inputs and/or cause thedrafted composite activity sequence to be embedded in the digitalactivity-accelerator registry.

2.15 Converting Tasks of a Composite Activity Sequence toComputer-Executable Instructions

Optionally, S215, which includes encoding a composite activity sequencewith computer executable code, may function to convert tasks of a targetcomposite activity sequence partially or completely tocomputer-executable instructions. In one or more embodiments, S215 mayfunction to compute or define computer-executable instructions for agiven task of a composite activity sequence and in one or moreembodiments, encode a display element representing each respective taskswith a distinct computer-executable instructions that, when selected andexecuted, automatically performs the respective task.

As non-limiting examples, S210 may function to generatecomputer-executable instructions that, when selected and executed,generates a digital document (e.g., an engagement document), retrievesdigital data and/or a digital file, composes content, auto populates adigital document, creates a new file (or client matter), and/or thelike.

2.20 Computing Activity-Accelerating Inferences

S220, which includes computing activity-accelerating inferences, mayfunction to compute activity-accelerating inferences for an identified(or sourced) user data artifact or user digital activity data. In apreferred embodiment, the one or more activity-accelerating inferencesmay be computed for a user data artifact comprising one or more digitalmessages received, drafted, and/or sent by a target user (e.g., textmessages, email messages, instant messages, or the like), calendarappointment data related to the target user, and/or the like. In one ormore embodiments, the one or more activity-accelerating inferencesrelate to a machine learning computed inference or prediction thatidentifies a likely activity classification or a likely intentclassification of an activity being performed by a user for completingsystem- or service-recognized objective.

Accordingly, in some embodiments, computing activity-acceleratinginferences may include establishing a secure connection to one or moredigital accounts of the target user (e.g., messaging accounts, emailaccounts, calendar scheduling accounts, and/or the like) and, in turn,computing activity-accelerating inferences based on one or more digitalartifacts existing in such digital accounts. In some examples, uponestablishing a secure connection to one or more digital accounts of thetarget user, S220 may function to compute activity-acceleratinginferences for each new user artifact detected in the one or moredigital accounts (e.g., for each new received or sent email message,text message, calendar invitation, etc.). Alternatively, in anotherexample, S220 may function to only compute activity-acceleratinginferences for user artifacts that satisfy inference-generation criteria(e.g., only compute activity-accelerating inferences for new incoming(email) messages or after a collection or detection of activitysatisfying a minimum activity threshold).

Ensemble of Subscriber-Specific Machine Learning Models

In one implementation, S220 may function to compute theactivity-accelerating inferences via an ensemble of subscriber-specificmachine learning models. Accordingly, in such an implementation,computing the activity-accelerating inferences may include extracting aplurality of features from a target user artifact and/or includeproviding those extracted plurality of features as input to the ensembleof subscriber-specific machine learning models. It shall be noted that,in some portions of the disclosure, a target user artifact may bereferred to as a “target digital artifact,” “a target artifact,” or thelike.

In some examples, the input provided to the ensemble ofsubscriber-specific machine learning models may comprise a compositevector (e.g., numerical) representation of a target user artifact. Forinstance, in a non-limiting example, if the target user artifactcorresponds to a user email message, generating a composite vectorrepresentation of that email message may include computing a numericalrepresentation of a subject of that email message; computing a numericalrepresentation of a body of that email message; computing a numericalrepresentation of a sender and/or recipient(s) of that email message;computing a numerical representation for one or more attachmentsincluded in that email message; aggregating/concatenating the one ormore of the numerical representations into a single vectorrepresentation; and/or the like.

It shall be noted that composite vector representations for other typesof user artifacts (e.g., calendar appointments, text messages, etc.) maybe computed, by S220, in one or more similar ways described above.

Models Underpinning the Subscriber-Specific Machine Learning Ensemble

In some embodiments, the machine learning models included in thesubscriber-specific machine learning ensemble may be based on one ormore characteristics or attributes of the subscriber and/or may betrained using one or more corpora of training data samples extractedfrom digital activities performed by the subscriber. In one example, thesubscriber-specific machine learning ensemble may be constructed basedat least on training data derived from a domain or industry relating tothe subscriber (e.g., a first attribute of the subscriber). Forinstance, in a non-limiting example, if the subscriber relates to afirst industry/domain (e.g., legal domain), S220 may function toimplement a machine learning ensemble that comprises a plurality oflegal domain-specific machine learning models (e.g., a collection ofmachine learning models trained using corpora of data samples relatingto the legal domain and the like). Conversely, if the subscriber relatesto a second industry/domain (e.g., accounting domain), S220 may functionto implement a machine learning ensemble that comprises a plurality ofaccounting domain-specific machine learning models.

Activity-Accelerating Inferences

The below will describe example machine learning models that may beincluded in a subscriber-specific machine learning ensemble. However, itshall be recognized that the below description is not intended to belimiting and that a subscriber-specific machine learning ensemble mayimplement more, fewer, or different machine learning models than theones described below without departing from the scope of theinvention(s) contemplated herein.

In a first implementation, a subscriber-specific machine learning modelmay implement one or more machine learning models specificallyconfigured to estimate/predict an intent of a user based on a providedinput (e.g., a composite vector representation of a user artifact).Accordingly, in such an implementation and based on S220 providing avector representation of a target user artifact to thesubscriber-specific machine learning ensemble as input, the one or moremachine learning models may function to compute one or moreactivity-accelerating inferences relating to a prediction/estimation ofa subsequent action to be performed by the user (e.g., predict/estimatethe immediate next action of the user, such as draft a client-specific(legal) document, file a client-specific (legal) document, beginperforming due-diligence activities, send an inquiry message to advisingcounsel, send a reply message to a sender associated with the userartifact, etc.).

It shall be noted that, in some embodiments, the intent-informativeactivity-accelerating inferences may additionally, or alternatively,indicate an urgency associated with the user artifact, paymentpreferences specified in the user artifact, outcomes desired by a senderassociated with the user artifact, and/or the like.

In a second implementation, the subscriber-specific machine learningmodel may additionally, or alternatively, implement one or more machinelearning models specifically configured to estimate/predict a practicearea (e.g., domain) associated with a target user artifact. Accordingly,in such an implementation and based on S220 providing a vectorrepresentation of the target user artifact to the subscriber-specificmachine learning ensemble as input, the one or more machine learningmodels may function to compute one or more activity-acceleratinginferences relating to a prediction/estimation of a practice areaassociated with the target user artifact (e.g., intellectual property,bankruptcy law, corporate law, etc.).

In a third implementation, the subscriber-specific machine learningmodel may additionally, or alternatively, implement one or more machinelearning models specifically configured to estimate/predict asub-practice (e.g., sub-domain) relating to a target user artifact.Accordingly, in such an implementation and based on S220 providing avector-based representation of the target user artifact to thesubscriber-specific machine learning ensemble as input, the one or moremachine learning models may function to compute one or moreactivity-accelerating inferences relating to a prediction/estimation ofa sub-practice associated with the target user artifact (e.g., to whichintellectual property sub-domain the user artifact relates: patents,trademarks, etc.).

In a fourth implementation, the subscriber-specific machine learningmodel may additionally, or alternatively, implement one or more machinelearning models specifically configured to extract probative informationfrom the target user artifact. For instance, in a non-limiting example,based on the subscriber-specific machine learning ensemble receiving avector representation of a target user artifact, the one or more machinelearning models may function to extract one or more types of entitiesnamed in the target user artifact (e.g., company name, company address,company phone number, client name, time expressions, locations, and/orthe like).

2.30 Identifying Germane Activity-Accelerating Actions

S230, which includes identifying germane activity-accelerating actions,may function to identify activity-accelerating actions germane/relevantto a target user artifact based on the activity-accelerating inferencescomputed in S220 and the digital activity-accelerator registryconstructed in S210. In a preferred embodiment, identifyingactivity-accelerating actions germane to a target user artifact (e.g., auser email message) may include identifying a target composite activitysequence in the digital activity-accelerator registry that correspondsto the target user artifact and/or may include which of theactivities/tasks included in the target composite activity sequence aregermane to the target piece of user data.

Composite Activity Sequence Storage Repository

In some embodiments, to identify which composite activity sequence inthe digital activity-accelerator registry relates to the target userartifact, S230 may function to search one or more data structures,lookup tables, and/or databases that store distinct entriescorresponding to each composite activity sequence stored in the digitalactivity-accelerator registry. In one example, each distinct entry mayinclude attributes relating to the composite activity sequence that itcorresponds to, such as the unique identifier assigned to such compositeactivity sequence, the name assigned to such composite activitysequence, the practice area assigned to such composite activitysequence, the sub-practice area assigned to such composite activitysequence, the activities/tasks comprising such composite activitysequence, and/or the like.

It shall be noted that, in some examples, the digitalactivity-accelerator registry (described in S210) may comprise theabove-described data structures, lookup tables, and/or databases. Thus,in some examples, identifying relevant activity-accelerating actions mayinclude searching the digital activity-accelerator registry in addition,or as an alternative, to querying/searching one or more other datastorage modules/systems. Furthermore, it shall also be noted that, insome portions of the disclosure, the above-described data structures,lookup tables and/or databases may be referred to as “a compositeactivity sequence storage repository.”

Constructing a Search Query

In some embodiments, S230 may function to construct a search query tosearch the composite activity sequence storage repository. In oneexample of such embodiments, constructing the search query may include(automatically) generating search criteria/parameters for the searchquery and annotating the search query to include such searchcriteria/parameters.

In some embodiments, automatically generating search parameters/criteriafor a search query may include automatically generating a searchcriterion/parameter based on the activity-accelerating inference relatedto predicted user intent (computed in S220). In one embodiment,generating such a search criterion may include constructing anexpression (e.g., Boolean expression) that causes the search query tosearch for composite activity sequences that include a task or actionequivalent (or semantically similar) to the estimated/predicted intentof the user. For instance, in a non-limiting example, if S220 computedan activity-accelerating inference indicating that, based on a targetuser artifact, the immediate next action of the target user correspondsto “drafting a client engagement letter,” S230 may function to constructa search criterion/parameter that causes the search query to only searchfor composite activity sequences that comprise a task/activity relatingto “drafting a client engagement letter.”

In some embodiments, automatically generating search criteria for asearch query may additionally, or alternatively, include generating asearch criterion based on the activity-accelerating inferences relatingto the predicted/estimated practice area associated with a target userartifact (computed in S220). In one embodiment, generating such a searchcriterion may include constructing an expression (e.g., Booleanexpression) that causes the search query to search for compositeactivity sequences—in the composite activity sequence storagerepository—that only belong to the predicted/estimated practice area ofa target user artifact. For instance, in a non-limiting example, if S220computed an activity-accelerating inference indicating that a targetpiece of user data relates to the “Intellectual Property” practice area,S230 may function to construct a search criterion that causes the searchquery to only search for composite activity sequences that belong to the“Intellectual Property” practice area.

In some embodiments, automatically generating search criteria for asearch query may additionally, or alternatively, include generating asearch criterion based on the activity-accelerating inference relatingto the predicted/estimated sub-practice area associated with a targetuser artifact (computed in S220). In one embodiment, generating such asearch criterion may include constructing an expression (e.g., Booleanexpression) that causes the search query to search for compositeactivity sequences—in the composite activity sequence storagerepository—that belong to the predicted/estimated sub-practice areaassociated with the target user artifact. For instance, in anon-limiting example, if S220 computed an activity-acceleratinginference indicating that a target user artifact relates to the “Patent”sub-practice area, S230 may function to construct a search criterionthat causes the search query to only search for composite activitysequences that belong to the “Patent” sub-practice area.

In some embodiments, automatically generating search criteria for asearch query may additionally, or alternatively, include generating oneor more search criteria based on probative information extracted fromthe target user artifact (described in S220). In one embodiment,generating the one or more search criteria may include constructing oneor more search expressions (e.g., Boolean expressions) that cause thesearch query to search for composite activity sequences—in the compositeactivity sequence storage repository—that have been created by the userassociated with the target user artifact, that have been created by abusiness/organization (e.g., a business administrator) associated withthe user, that corresponds to the client associated with the target userartifact, and/or the like.

Executing a Search Query

Additionally, or alternatively, in some embodiments, as generally shownin FIG. 3 , S230 may function to execute the constructed activitysequence search query. Executing the constructed search query (asbriefly mentioned above) may cause S240 to search a composite activitysequence storage repository for one or more composite activity sequencesthat satisfy the search criteria/parameters of the search query. In someembodiments, if S230 finds a composite activity sequence satisfying thesearch criteria/parameters of the search query, S230 may, in turn,return that composite activity sequence as a search result. Otherwise,in some embodiments, if S230 does not find a composite activity sequencesatisfying the search criteria/parameters of the search query, S230 maynot return any composite activity sequence as result of executing thesearch query.

Accordingly, in some embodiments, the activity-accelerating actions thatS230 determines to be germane to a target piece of user data may bebased on results returned from executing the search query. For instance,in one example of such embodiments, based on a first composite activitysequence being returned from a search query, S230 may function toidentify at least a subset of the actions/tasks included in the firstcomposite activity sequence as being germane to the target userartifact.

In a preferred embodiment, the actions/tasks that S230 determines to begermane to the target user artifact may only include actions/tasks thatmatch or relate to the estimated intent of the user and/or only includeactions/tasks that may be executed after (e.g., subsequent to) theaction/task matching the estimated intent of the user.

Furthermore, in some embodiments, in the event that no compositeactivity sequence is returned from the search query, S230 may functionto determine that no composite activity sequence in the compositeactivity sequence storage repository is germane to the target piece ofuser data and thus, may identify zero actions/tasks as being germane tothe target piece of user data (and thus, may cause the method 200 toforgo performing one or more processes described in S240).

2.40 Executing Germane Activity-Accelerating Actions

S240, which includes executing germane activity-accelerating actions,may function to automatically execute one or more of theactivity-accelerating actions determined, in S230, to be germane to atarget piece of user data.

In some embodiments, S240 may function to execute one or moreactivity-accelerating actions based on user input. In one example ofsuch embodiments, to interface with a user, S240 may function to displaya graphical user interface that includes one or more selectablerepresentations corresponding to the one or more activity-acceleratingactions determined, in S230, to be germane to a target piece of userdata. Each selectable representation displayed in the graphical userinterface, when selected, may cause S240 to execute (or initiate aprocess to execute) the activity-accelerating action corresponding tothat selected representation (described in more detail below).

It shall be noted that, while the above embodiment(s) describe examplesof executing activity-accelerating actions based on user input, S240 mayadditionally, or alternatively, function to automatically execute (e.g.,without user input) one or more of the activity-accelerating actionsdetermined, in S230, to be germane to a target user artifact.Furthermore, it shall also be noted that displaying germaneactivity-accelerating actions to a target user may result in manytechnical benefits including, but not limited, enabling knowledgetransfer between different entities (e.g., employees) of anorganization, promoting process/task consistency across employees of anorganization, and/or the like.

Automatically Composing/Generating Documents

In some embodiments, executing a target activity-accelerating action mayinclude automatically composing/generating one or more documents oremails. For instance, in a non-limiting example, if S240 is executing anactivity-accelerating action corresponding to “draft a client engagementletter,” S240 may function to automatically compose (generate) one ormore documents and/or emails relating to drafting a client engagementletter.

In some embodiments, automatically composing/generating one or moredocuments or emails may include identifying one or more document-basedand/or email-based templates corresponding to the activity-acceleratingaction being executed. In one example of such embodiments, S240 mayfunction to identifying document-based and/or email-based templatescorresponding to a target activity-acceleration action by querying oneor more data structures, database tables and/or lookup tables thatcorrelate activity-accelerating actions to a target set ofdocument-based and/or email-based templates.

Accordingly, in such an example, S240 may function to search suchstorage repositories for an entry corresponding to the targetactivity-accelerating action and identify, based on that entry,document-based and/or email-based templates corresponding to the targetactivity-accelerating action.

In some embodiments, the document-based and/or email-based templatesidentified as corresponding to a target activity-accelerating action mayinclude one or more in-line document tags that indicate, to S240, thatthese in-line document tags should be replaced based on the probativeinformation extracted from the target user artifact in S220 and/or basedon the activity-accelerating inferences computed for the target userartifact in S220. For instance, in a non-limiting example, the one ormore document-based and/or email-based templates corresponding to thetarget activity-accelerating action may include one or more firstin-line document tags that indicate S240 should replace theses with thename of the company associated with the target user artifact (e.g.,{company_name}), may include one or more second in-line document tagsthat indicate S240 should replace these tags with the client matternumber associated with the target user artifact (e.g.,{client_matter_number}).

Additionally, or alternatively, in some embodiments, automaticallycomposing/generating one or more documents or emails may includeinstalling at locations corresponding to the in-line document tags databased on the probative information extracted from the target userartifact in S220 and/or based on the activity-accelerating inferencescomputed for the target user artifact in S220. For instance, in anon-limiting example, if S220 extracted that the target user artifact isassociated with client matter number 1027-50006.01 and associated withwith company ‘XYZ’, S240 may function to replace each in-line documenttag corresponding to “{company_name}” and “{client_matter_number}” withthe text ‘XYZ’ and ‘1027-50006.01’; respectively.

Improving Activity-Accelerating Recommendations

In some embodiments, S240 may additionally, or alternatively, functionto continuously adapt, modify, or improve the activity-acceleratingactions it recommends to a user based on observed behavior and/orpreferences of that user. For instance, in a non-limiting example, if auser decides to forgo executing one of the recommendedactivity-accelerating actions (or provides an indication that they wishto manually perform such action), S240 may function to de-prioritizethat activity-accelerating action when generating futureactivity-accelerating recommendations for that user (and/or that type oftarget user artifact). Conversely, if a user decides to execute one ofthe recommended activity-accelerating actions, S240 may function toprioritize that activity-accelerating action when generating futureactivity-accelerating recommendations for that user (and/or that type oftarget user artifact).

3. Computer-Implemented Method and Computer Program Product

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),concurrently (e.g., in parallel), or in any other suitable order byand/or using one or more instances of the systems, elements, and/orentities described herein.

Although omitted for conciseness, the preferred embodiments may includeevery combination and permutation of the implementations of the systemsand methods described herein.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for machine learning-informed surfacing andautomated execution of digital activity-accelerating actions, the methodcomprising: constructing a digital activity-accelerator registry thatincludes a plurality of composite activity sequences, wherein each ofthe plurality of composite activity sequences relates to a distinctcollection of interdependent computer-executable tasks; identifying atarget digital artifact; and based on identifying the target digitalartifact: computing, by one or more computers operating an ensemble ofmachine learning models, a plurality of activity-accelerating inferencesfor the target digital artifact, including: (1) a firstactivity-accelerating inference that relates to an estimated intent ofthe target digital artifact, (2) a second activity-acceleratinginference that relates to an estimated domain of an activity associatedwith the target digital artifact, and (3) a third activity-acceleratinginference that relates to an estimated sub-domain of the activityassociated with the target digital artifact; searching the digitalactivity-accelerator registry based on the plurality ofactivity-accelerating inferences, wherein searching the digitalactivity-accelerator registry includes: constructing a compositeactivity sequence search query that comprises a plurality of searchparameters, wherein the plurality of search parameters includes: (A) afirst search parameter that causes the search query, when executed, tosearch for composite activity sequences in the digitalactivity-accelerator registry that include a computer-executable taskrelating to the estimated intent of the target digital artifact, (B) asecond search parameter that causes the search query, when executed, tosearch for composite activity sequences in the digitalactivity-accelerator registry that relate to the estimated domain of theactivity associated with the target digital artifact, and (C) a thirdsearch parameter that causes the search query, when executed, to searchfor composite activity sequences in the digital activity-acceleratorregistry that relate to the estimated sub-domain of the activityassociated with the target user artifact; and executing the compositeactivity sequence search query, wherein executing the composite activitysequence causes one or more target composite activity sequences to bereturned from the digital activity-accelerator registry if the one ormore target composite activity sequences satisfy the plurality of searchparameters; extracting at least a subset of the computer-executabletasks included in the one or more target composite activity sequencesbased on one or more pre-defined action-surfacing criteria; anddisplaying, via a graphical user interface, a digital representationcorresponding to each computer-executable task included in the subset,wherein each digital representation displayed in the graphical userinterface is selectable and, when selected, causes a correspondingactivity-accelerating task to be automatically executed by the one ormore computers.
 2. The method of claim 1, wherein constructing thedigital activity-accelerator registry includes automatically creating,by the one or more computers, the plurality of composite activitysequences.
 3. The method of claim 2, wherein automatically creating theplurality of composite activity sequences includes: collecting digitalactivity data relating to one or more users over a respective period oftime, grouping the digital activity data into one or more sets ofinterdependent computer-executable tasks, instantiating a compositeactivity sequence corresponding to each of the one or more sets ofinterdependent computer-executable tasks, and embedding eachinstantiated composite activity sequence in the digitalactivity-accelerator registry.
 4. The method of claim 3, wherein thedigital activity data includes a first plurality of computer-executabletasks, and grouping the digital activity data into the one or more setsof interdependent computer-executable tasks includes: in accordance witha determination that the one or more users performed the first pluralityof computer-executable tasks in a same order for more than a thresholdnumber of times, grouping the first plurality of computer-executabletasks as a first set of interdependent computer-executable tasks; and inaccordance with a determination that the one or more users did notperform the first plurality of computer-executable tasks in a same orderfor more than the threshold number of times, forgoing grouping the firstplurality of computer-executable tasks.
 5. The method of claim 1,wherein the digital activity-accelerator registry includes (1) aplurality of composite activity sequences automatically derived by theone or more computers and (2) a plurality of user-created compositeactivity sequences.
 6. The method of claim 1, wherein: the one or moretarget composite activity sequences returned from executing thecomposite activity sequence search query includes a first targetcomposite activity sequence, and extracting the subset ofcomputer-executable tasks from the first target composite activitysequence includes: (1) extracting, from the first target compositeactivity sequence, a first computer-executable task corresponding to theestimated intent of the target digital artifact, and (2) extracting,from the first target composite activity sequence, one or more secondcomputer-executable tasks ordered after the computer-executable taskcorresponding to the estimated intent of the target digital artifact. 7.The method of claim 6, wherein computer-executable tasks ordered beforethe first computer-executable task are not extracted from the firsttarget composite activity sequence.
 8. The method of claim 1, whereinthe one or more target composite activity sequences satisfy theplurality of search parameters if: (1) the one or more target compositeactivity sequences include a computer-executable task relating to theestimated intent of the target digital artifact, (2) the one or moretarget composite activity sequences relate to the estimated domain ofthe activity associated with the target digital artifact, and (3) theone or more target composite activity sequences relate to the estimatedsub-domain of the activity associated with the target digital artifact.9. The method of claim 1, wherein: the method is performed at acomputer-aided task automation service, and the target digital artifactcomprises a digital email message.
 10. The method of claim 9, whereinthe target digital artifact is identified within a threshold amount oftime of a digital messaging application sending the digital emailmessage.
 11. The method of claim 9, wherein the target digital artifactis identified within a threshold amount of time of a digital messagingapplication receiving the digital email message.
 12. The method of claim1, wherein displaying the graphical user interface includes displayingthe digital representation corresponding to each computer-executabletask included in the subset concurrently with a digital representationof the target digital artifact.
 13. The method of claim 1, wherein thegraphical user interface includes a first digital representationcorresponding to a first computer-executable task, the method furthercomprising: receiving an input selecting the first digitalrepresentation; and based on receiving the input: searching a lookuptable that correlates computer-executable tasks to document templates;and in accordance with a determination that the lookup table includes anentry corresponding to the first computer-executable task, automaticallygenerating one or more user-specific documents.
 14. The method of claim13, further comprising: in accordance with a determination that thelookup table does not include the entry corresponding to the firstcomputer-executable task, forgoing automatically generating one or moreuser-specific documents.
 15. The method of claim 13, whereinautomatically generating one or more user-specific documents includes:identifying, based on the entry corresponding to the firstcomputer-executable task, a plurality of document templatescorresponding to the first computer-executable task; instantiating,based on the plurality of document templates, a plurality ofuser-specific documents; extracting, via an entity extraction machinelearning model, a plurality of named entities from the target digitalartifact; and installing, at corresponding portions in the plurality ofuser-specific documents, one or more of the plurality of named entities.